The best use of technology in the health emergency operation centers during COVID-19 pandemic
Bibliographic record
Abstract
Since the outbreak of the coronavirus in the Kingdom of Saudi Arabia in 2020, National Health Emergency Center aligned itself to the Saudi Arabia's Vision 2030 and has played a key role to link the different health sectors in the country with Ministry of Health, through the use of state-of-the-art infrastructure, innovative digital technologies, location intelligence, data analysis, and real time data. Thereby, General Directorate of Emergency, Disasters and Medical Transportation - Deputyship of Curative Services, launched the National Health Emergency Operation Center, which integrates digital technologies to deliver substantial improvements to emergency healthcare management. Through real-time maps, apps, and dashboards, the innovative integration of different technologies has revolutionized the Center's operations by providing location intelligence and evidence-based analysis that shapes sound policy and saves lives. Disaster health management has become a key goal for every nation in order to reduce the impact of disasters on human health and wellbeing. It is an important aspect of any resilient healthcare system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".